Accelerated Stochastic ADMM with Variance Reduction

Alternating Direction Method of Multipliers (ADMM) is a popular method in solving Machine Learning problems. Stochastic ADMM was firstly proposed in order to reduce the per iteration computational complexity, which is more suitable for big data problems. Recently, variance reduction techniques have been integrated with stochastic ADMM in order to get a fast convergence rate, such as SAG-ADMM and SVRG-ADMM,but the convergence is still suboptimal w.r.t the smoothness constant. In this paper, we propose a new accelerated stochastic ADMM algorithm with variance reduction, which enjoys a faster convergence than all the other stochastic ADMM algorithms. We theoretically analyze its convergence rate and show its dependence on the smoothness constant is optimal. We also empirically validate its effectiveness and show its priority over other stochastic ADMM algorithms

Energy-Efficient Non-Orthogonal Transmission under Reliability and Finite Blocklength Constraints

This paper investigates an energy-efficient non-orthogonal transmission design problem for two downlink receivers that have strict reliability and finite blocklength (latency) constraints. The Shannon capacity formula widely used in traditional designs needs the assumption of infinite blocklength and thus is no longer appropriate. We adopt the newly finite blocklength coding capacity formula for explicitly specifying the trade-off between reliability and code blocklength. However, conventional successive interference cancellation (SIC) may become infeasible due to heterogeneous blocklengths. We thus consider several scenarios with different channel conditions and with/without SIC. By carefully examining the problem structure, we present in closed-form the optimal power and code blocklength for energy-efficient transmissions. Simulation results provide interesting insights into conditions for which non-orthogonal transmission is more energy efficient than the orthogonal transmission such as TDMA.Comment: accepted by IEEE GlobeCom workshop on URLLC, 201

Generative Actor-Critic: An Off-policy Algorithm Using the Push-forward Model

Model-free deep reinforcement learning has achieved great success in many domains, such as video games, recommendation systems and robotic control tasks. In continuous control tasks, widely used policies with Gaussian distributions results in ineffective exploration of environments and limited performance of algorithms in many cases. In this paper, we propose a density-free off-policy algorithm, Generative Actor-Critic(GAC), using the push-forward model to increase the expressiveness of policies, which also includes an entropy-like technique, MMD-entropy regularizer, to balance the exploration and exploitation. Additionnally, we devise an adaptive mechanism to automatically scale this regularizer, which further improves the stability and robustness of GAC. The experiment results show that push-forward policies possess desirable features, such as multi-modality, which can improve the efficiency of exploration and asymptotic performance of algorithms obviously

Efficient Cross-Device Federated Learning Algorithms for Minimax Problems

In many machine learning applications where massive and privacy-sensitive data are generated on numerous mobile or IoT devices, collecting data in a centralized location may be prohibitive. Thus, it is increasingly attractive to estimate parameters over mobile or IoT devices while keeping data localized. Such learning setting is known as cross-device federated learning. In this paper, we propose the first theoretically guaranteed algorithms for general minimax problems in the cross-device federated learning setting. Our algorithms require only a fraction of devices in each round of training, which overcomes the difficulty introduced by the low availability of devices. The communication overhead is further reduced by performing multiple local update steps on clients before communication with the server, and global gradient estimates are leveraged to correct the bias in local update directions introduced by data heterogeneity. By developing analyses based on novel potential functions, we establish theoretical convergence guarantees for our algorithms. Experimental results on AUC maximization, robust adversarial network training, and GAN training tasks demonstrate the efficiency of our algorithms

Efficient Projection-Free Online Methods with Stochastic Recursive Gradient

This paper focuses on projection-free methods for solving smooth Online Convex Optimization (OCO) problems. Existing projection-free methods either achieve suboptimal regret bounds or have high per-iteration computational costs. To fill this gap, two efficient projection-free online methods called ORGFW and MORGFW are proposed for solving stochastic and adversarial OCO problems, respectively. By employing a recursive gradient estimator, our methods achieve optimal regret bounds (up to a logarithmic factor) while possessing low per-iteration computational costs. Experimental results demonstrate the efficiency of the proposed methods compared to state-of-the-arts.Comment: 15 pages, 3 figure